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Scipy.stats.ttest_ind() function
- To perform a t-test in Python, you can use the scipy.stats.ttest_ind() function like t_statistic, p_value = stats.ttest_ind(data1, data2). This function allows you to compare two independent data sets and returns the t-statistic and the p-value.
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Feb 12, 2024 · In this post, you’ll learn how to perform t-tests in Python using the popular SciPy library. T-tests are used to test for statistical significance and can be hugely advantageous when working with smaller sample sizes.
Jul 25, 2023 · A t-test is a statistical method that’s used to determine whether there is a significant difference between the means of two groups. Here’s how to do it in Python.
- Machine Learning Engineer
- 20 min
Feb 24, 2010 · I'm looking to generate some statistics about a model I created in python. I'd like to generate the t-test on it, but was wondering if there was an easy way to do this with numpy/scipy. Are there any good explanations around? For example, I have three related datasets that look like this: [55.0, 55.0, 47.0, 47.0, 55.0, 55.0, 55.0, 63.0]
- Terminology Explained
- T-test Assumptions
- One-Sample t-test
- How to Perform Two-Sample t-test in Python
- How to Perform Paired t-test in Python
- How to Perform Welch's t-test in Python
- Conclusion
- References and Recommended Resources
Before we delve deeper into the details of the t-test, let us quickly understand some of the associated terminologies that will help strengthen your conceptual grasp of this statistical test.
Understanding the assumptions of a statistical test is crucial to ensure accurate and reliable results. The t-test is no exception. Any violation of its assumptions could lead to misleading conclusions, which would be counterproductive. Let's explore the four assumptions of the t-test in detail:
The one-sample t-test is a statistical hypothesis test that helps determine if an unknown population mean (mu) does not equal a claimed value. Where, x = sample mean 𝝁 = population mean S = sample standard deviation n = number of examples in the sample t = t-statistic Let’s understand this by using an example. A company claims to produce ball bear...
Let’s extend our example by assuming that the company sets up another factory to produce identical ball bearings. We need to find out if the ball bearings from the two factories are of different sizes. For such a scenario, we use the two-sample test. Here the t-statistic is defined as below. Where, X1= first sample mean X2 = second sample mean S1= ...
Upon sharing these results with the company, it decides to improve its manufacturing technology by introducing a new casting machine. It starts this pilot from one of the factories and conducts a test to identify if the new casting machine leads to a change in the diameter of the bearings by comparing two samples of 25 bearings – one before the new...
After a successful pilot from the new casting machine at one factory, the company wants to try another pilot at its second factory with a new rubbing machine. This machine is known to produce highly accurate ball bearings but is affected by temperature changes and power fluctuations. The company needs to identify if the ball bearings from the two f...
Data has the power to uncover the true underlying phenomenon impacting business decisions only if interrogated rightly. It requires a robust understanding of different statistical tests to know which one to apply and when. This tutorial focuses on t-tests and explains their underlying assumptions, such as independent and identically distributed obs...
Import the ttest_rel function from the stats library to perform a dependent sample t-test (paired t-test).
Aug 19, 2022 · There are four types of T test you can perform in Python. They are as follows: One sample T test. Two sample T test (paired) Two sample T test (independent) Welch T test. Let’s understand each of the tests and how we can implement every single of the tests accordingly.
Sep 13, 2023 · To perform a t-test in Python, you can use the scipy.stats.ttest_ind() function like t_statistic, p_value = stats.ttest_ind(data1, data2). This function allows you to compare two independent data sets and returns the t-statistic and the p-value. Here’s a simple example: from scipy import stats. data1 = [1, 2, 3, 4, 5] data2 = [6, 7, 8, 9, 10]
